Naslund Chapter 1 Analysis

Laura Naslund

2023-02-10

Chapter 1 Sampling scheme

We sampled 4 sites over at least one 24 hr period. We installed 25 ebullition traps across 5 transects in each impoundment. Every 3 hours, we recorded the volume of gas accumulated in each ebullition trap and took a diffusive flux measurement immediately adjacent to 10 if the ebullition traps across the impoundment. To determine impoundment area, I digitized the impoundments using high resolution (0.5 ft) orthoimagery captured during the leaf off period. Pick has a beaver dam on the inlet stream and I delineated only to the beaver dam.

mapshot(
 mapview(deans, col.regions = "#2D708EFF", alpha.regions = 1, legend = FALSE, homebutton=FALSE)+
  mapview(catfish, col.regions = "#2D708EFF", alpha.regions = 1, legend = FALSE, homebutton=FALSE)+
  mapview(sister3, col.regions = "#2D708EFF", alpha.regions = 1, legend = FALSE, homebutton=FALSE)+
  mapview(pick, col.regions = "#2D708EFF", alpha.regions = 1, legend = FALSE, homebutton=FALSE),
  file = "site_map.png",
 remove_controls = c("zoomControl", "layersControl", "homeButton", 
    "drawToolbar", "easyButton"),
)

Site	Area (km2)	Max Depth (m)	Mean Depth (m)	Residence Time (days)	Dates Sampled
Deans	0.0041	3.52	2.03	46	8/16/22 - 8/17/22 8/30/22 - 8/31/22
Sister 3	0.0012	2.27	1.25	20	8/22/22 - 8/23/22
Catfish	0.0018	1.98	0.80	19	9/06/22 - 9/07/22 9/18/22 - 9/19/22
Pick	0.0077	3.80	1.58	45	8/22/22 - 8/23/22

1.1 Dissolved oxygen

We recorded dissolved oxygen every 15 minutes 0.25 m below the surface in every impoundment. From 6/20/22 to 7/13/22, we also recorded dissolved oxygen 0.1m from the bottom of Pick in the deepest location in the impoundment. Because dissolved oxygen was consistently 0 mg/L, we reallocated our oxygen sensors and installed both a top and bottom DO sensor in Sister 3.

1.1.1 Deans

1.1.2 Catfish

1.1.3 Sister 3

1.1.4 Pick

1.1.5 All impoundments top DO

1.2 Temperature

1.2.1 Deans

1.2.2 Catfish

1.2.3 Sister 3

1.2.4 Pick

1.3 PAR

All sensors were submerged ~0.25m below the water surface

1.3.1 Deans

Sensor casing cracked, so no available PAR data for second Deans sampling

dygraph(deans.xts) %>% 
  dyOptions(colors = "#DCE319FF") %>% 
  dyAxis("y", label = "PAR (umol/m2/s)")

8/16/22

deans16.xts <- as.xts(par %>% filter(site == "Deans", date_time > ymd_hms("2022-08-16 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-08-17 09:50:00", tz = "America/New_York")) %>% pull(calibrated), order.by = par %>% filter(site == "Deans", date_time > ymd_hms("2022-08-16 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-08-17 09:50:00", tz = "America/New_York")) %>% pull(date_time))

dygraph(deans16.xts) %>% 
  dyOptions(colors = "#DCE319FF") %>% 
  dyAxis("y", label = "PAR (umol/m2/s)")

1.3.2 Catfish

dygraph(catfish.xts) %>% 
  dyOptions(colors = "#55C667FF") %>% 
  dyAxis("y", label = "PAR (umol/m2/s)")

9/6/22

catfish06_same.xts <-
  as.xts(
    par %>% filter(
      site == "Catfish",
      date_time > ymd_hms("2022-09-06 06:00:00", tz = "America/New_York"),
      date_time < ymd_hms("2022-09-07 12:00:00", tz = "America/New_York")
    ) %>% mutate(
      date_time_same = if_else(date == "06/09/2022 ", paste0("1900-01-01", time) %>% ymd_hms(tz = "America/New_York"), paste0("1900-01-02", time) %>% ymd_hms(tz = "America/New_York"))
    ) %>% pull(calibrated),
    order.by = par %>% filter(
      site == "Catfish",
      date_time > ymd_hms("2022-09-06 06:00:00", tz = "America/New_York"),
      date_time < ymd_hms("2022-09-07 12:00:00", tz = "America/New_York")
    ) %>% mutate(
      date_time_same = if_else(date == "06/09/2022 ", paste0("1900-01-01", time) %>% ymd_hms(tz = "America/New_York"), paste0("1900-01-02", time) %>% ymd_hms(tz = "America/New_York"))
    ) %>% pull(date_time_same)
  )

dygraph(catfish06_same.xts)

catfish18_same.xts <-
  as.xts(
    par %>% filter(
      site == "Catfish",
      date_time > ymd_hms("2022-09-18 06:00:00", tz = "America/New_York"),
      date_time < ymd_hms("2022-09-19 12:00:00", tz = "America/New_York")
    ) %>% mutate(
      date_time_same = if_else(date == "18/09/2022 ", paste0("1900-01-01", time) %>% ymd_hms(tz = "America/New_York"), paste0("1900-01-02", time) %>% ymd_hms(tz = "America/New_York"))
    ) %>% pull(calibrated),
    order.by = par %>% filter(
      site == "Catfish",
      date_time > ymd_hms("2022-09-18 06:00:00", tz = "America/New_York"),
      date_time < ymd_hms("2022-09-19 12:00:00", tz = "America/New_York")
    ) %>% mutate(
      date_time_same = if_else(date == "18/09/2022 ", paste0("1900-01-01", time) %>% ymd_hms(tz = "America/New_York"), paste0("1900-01-02", time) %>% ymd_hms(tz = "America/New_York"))
    ) %>% pull(date_time_same)
  )

catfish_day.xts <- cbind(catfish06_same.xts, catfish18_same.xts)

pal <- c("#55C667FF", "#143C1B")
dygraph(catfish_day.xts) %>% 
  dyOptions(colors = pal) %>% 
  dyAxis("y", label = "PAR (umol/m2/s)")

1.3.3 Pick

dygraph(pick.xts) %>% 
  dyOptions(colors = "#440154FF") %>% 
  dyAxis("y", label = "PAR (umol/m2/s)")

#9/13/22

pick13.xts <- as.xts(par %>% filter(site == "Pick", date_time > ymd_hms("2022-09-13 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-09-14 12:00:00", tz = "America/New_York")) %>% pull(calibrated), order.by = par %>% filter(site == "Pick", date_time > ymd_hms("2022-09-13 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-09-14 12:00:00", tz = "America/New_York")) %>% pull(date_time))

dygraph(pick13.xts) %>% 
  dyOptions(colors = "#440154FF") %>% 
  dyAxis("y", label = "PAR (umol/m2/s)")

1.3.4 Sister3

dygraph(sister3.xts) %>% 
  dyOptions(colors = "#2D708EFF") %>% 
  dyAxis("y", label = "PAR (umol/m2/s)")

#8/22/22

sister322.xts <- as.xts(par %>% filter(site == "Sister3", date_time > ymd_hms("2022-08-22 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-08-23 12:00:00", tz = "America/New_York")) %>% pull(calibrated), order.by = par %>% filter(site == "Sister3", date_time > ymd_hms("2022-08-22 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-08-23 12:00:00", tz = "America/New_York")) %>% pull(date_time))

dygraph(sister322.xts) %>% 
  dyOptions(colors = "#2D708EFF") %>% 
  dyAxis("y", label = "PAR (umol/m2/s)")

1.4 Dissolved gas concentrations

I sampled for dissolved gas 0.25 m from the surface of the impoundments, 0.1m from the bottom, in the inlet stream, and in the outlet stream. The dissolved gas samples from Catfish on 9/18/22-9/19/22 were not handled properly, thus those values are excluded from the summaries.

gc <- read.csv("2-Clean Data/dissolved-gas.csv")

gc_summarized <- gc %>% mutate_if(is.character, 
                str_replace_all, 
                pattern = "Picks", 
                replacement = "Pick") %>% 
  mutate_if(is.character, 
                str_replace_all, 
                pattern = "oulet", 
                replacement = "outlet")%>% 
  group_by(Site, Collection.Date, Location) %>% 
  summarize(CH4_avg_umol = mean(Original_Liq_CH4_umol), CO2_avg_umol = mean(Original_Liq_CO2_umol), CH4_sd = sd(Original_Liq_CH4_umol, na.rm = T), CO2_sd = sd(Original_Liq_CO2_umol, na.rm = T), reps = n()) %>% 
  mutate(Location = factor(Location, levels = c("top","bottom", "inlet", "outlet")))

## `summarise()` has grouped output by 'Site', 'Collection.Date'. You can override using the `.groups`
## argument.

gc_summarized %>% filter(mdy(Collection.Date)>mdy("08/15/22"), Collection.Date!="9/19/2022") %>% select(-CO2_avg_umol, -CO2_sd) %>%  arrange(Location, desc(CH4_avg_umol))

## # A tibble: 20 × 6
## # Groups:   Site, Collection.Date [5]
##    Site    Collection.Date Location CH4_avg_umol     CH4_sd  reps
##    <chr>   <chr>           <fct>           <dbl>      <dbl> <int>
##  1 Pick    9/13/2022       top           98.7     3.09          3
##  2 Deans   8/31/2022       top            0.980   1.32          3
##  3 Sister3 8/23/2022       top            0.849   0.0111        3
##  4 Catfish 9/6/2022        top            0.586   0.00180       3
##  5 Deans   8/16/2022       top            0.583   0.0476        2
##  6 Pick    9/13/2022       bottom       126.     60.7           3
##  7 Sister3 8/23/2022       bottom         2.89    0.985         3
##  8 Deans   8/16/2022       bottom         0.883   0.0543        3
##  9 Catfish 9/6/2022        bottom         0.557   0.0686        3
## 10 Deans   8/31/2022       bottom         0.290   0.0597        3
## 11 Deans   8/31/2022       inlet         16.9     4.77          3
## 12 Pick    9/13/2022       inlet          9.98    1.34          3
## 13 Deans   8/16/2022       inlet          9.95    2.86          3
## 14 Catfish 9/6/2022        inlet          0.598   0.197         3
## 15 Sister3 8/23/2022       inlet          0.0229  0.0000664     3
## 16 Deans   8/31/2022       outlet         4.31    0.936         3
## 17 Sister3 8/23/2022       outlet         2.62    0.0720        3
## 18 Catfish 9/6/2022        outlet         1.47    0.410         3
## 19 Pick    9/13/2022       outlet         0.365   0.170         3
## 20 Deans   8/16/2022       outlet         0.311   0.106         3

gc_summarized %>% filter(mdy(Collection.Date)>mdy("08/15/22"), Collection.Date!="9/19/2022") %>% select(-CH4_avg_umol, -CH4_sd) %>%arrange(Location, desc(CO2_avg_umol))

## # A tibble: 20 × 6
## # Groups:   Site, Collection.Date [5]
##    Site    Collection.Date Location CO2_avg_umol CO2_sd  reps
##    <chr>   <chr>           <fct>           <dbl>  <dbl> <int>
##  1 Pick    9/13/2022       top             560.    8.69     3
##  2 Catfish 9/6/2022        top             138.    5.69     3
##  3 Deans   8/16/2022       top             107.    3.11     2
##  4 Deans   8/31/2022       top              62.3   7.88     3
##  5 Sister3 8/23/2022       top              40.3   4.55     3
##  6 Pick    9/13/2022       bottom          635.  161.       3
##  7 Catfish 9/6/2022        bottom          164.   49.1      3
##  8 Deans   8/16/2022       bottom           88.9  16.4      3
##  9 Sister3 8/23/2022       bottom           70.0  15.2      3
## 10 Deans   8/31/2022       bottom           36.6   6.79     3
## 11 Deans   8/31/2022       inlet           377.   99.1      3
## 12 Deans   8/16/2022       inlet           234.   46.0      3
## 13 Pick    9/13/2022       inlet           168.    9.29     3
## 14 Catfish 9/6/2022        inlet           141.    6.10     3
## 15 Sister3 8/23/2022       inlet            75.2   7.54     3
## 16 Pick    9/13/2022       outlet          160.   25.6      3
## 17 Deans   8/31/2022       outlet          153.   44.1      3
## 18 Catfish 9/6/2022        outlet          142.    9.15     3
## 19 Sister3 8/23/2022       outlet           81.8   8.42     3
## 20 Deans   8/16/2022       outlet           50.2   5.51     3

1.5 Residence time calculation

I took a point estimate of discharge from the ponds while sampling, but these discharge values gave unrealistic residence times. I instead calculated discharge from a regional regression equation using average annual precipitation and watershed area (Gotvald et al. 2017).